Boundary-Guided Probability Hashing

Wenjin Hu, Yukun Chen, Lifang Wu, Ge Shi, Meng Jian, Sinuo Deng

Abstract: Deep supervised hashing for Hamming space retrieval has recently attracted increasing attention because it enables large-scale image retrieval with constant-time cost. However, the existing Hamming space retrieval methods cannot effectively focus on different pairs simultaneously inside and outside the Hamming ball, making it difficult to push dissimilar pairs outside or pull similar pairs inside the Hamming ball. We propose a novel Boundary-Guided Probability Hashing (BGPH) method that introduces a boundary to guide probability distribution. It makes the probability of similar pairs within the Hamming ball greater than dissimilar pairs and vice versa, which fits the purpose of Hamming space retrieval well. Moreover, we propose a threshold weighting method to indicate when optimization should be stopped to avoid the problem that dissimilar data are pulled into the ball caused by over-optimization in multi-label retrieval scenarios. Comprehensive experiments on three benchmark datasets demonstrate that BGPH yields state-of-the-art retrieval performance.